Genometric analysis of quantitative traits

方法开发 在过去的一年里，几乎所有的理论工作都集中在平铺回归的发展上，这是一种基于线性回归的方法，用于数量性状的家庭内关联测试，在标记和观测水平上都解决了非独立性问题。平铺回归在由热点块定义的基因组的预定义片段中使用多重和逐步回归方法，以识别分别导致数量性状和质量性状的变异或易感性的独立遗传变量。使用多种和逐步的方法来测试每个区块中的序列变体上的关联性，以选择每个区块中的独立标记。然后，使用高阶逐步回归来识别跨片、染色体和整个基因组的显著变异。可以对数量性状和质量性状进行分析。使用这种方法，分析数十万或数百万个标记及其重要的基因x基因互作项变得可行。这种方法可以将测试的总数大大减少到更接近平铺的数量，而不是标记的数量。此外，平铺方法可以合并到线性回归框架中，该框架允许不同观测值之间的非独立性，这些观测值结合了子代对中亲回归(ROMP)和广义估计方程方法的特征。 平铺回归方法已在TRAP中实现，这是一个用免费提供的R语言编写的软件包。提供了将SNP分配给基于热点的瓷砖、数据输入、结果分析和输出的功能。该程序包采用模块化结构，因此可以作为单个程序使用，也可以与用户编写的函数一起使用，以允许替代瓦片定义或数据格式。这种方法已经被应用于两个SNP数据，一个是与Douglas Stewart博士合作的NF1项目，另一个是与Les Biesecker博士合作的ClinSeq项目，这两个SNP数据来自与Nancy Miller(科罗拉多大学)合作的SNP研究和脊柱侧弯数据。 协作 家族性特发性脊柱侧凸 已经完成了对候选区域和表型亚组的几项分析，已提交或正在编写手稿。这些措施包括： 1)在至少有一个个体的FIS家系的易感基因座研究中，Marosy等人在6号和10号染色体上确定了候选区域。2010年。 2)两组家族性特发性脊柱侧凸家系的统计遗传分析，其特征与Miller等人分析的样本基本相同。2005年。在1号染色体上的两个区域进行了连锁分析和关联性测试，这两个区域以前被确定为主要候选区域。我们已经为后续的下一代测序确定了几个感兴趣的区域，Behnemann，博士论文。 3)对脊柱侧凸家系中的IRX基因家族进行定向测序。我们已经确定了脊柱后凸与IRX基因上游保守区的序列变异之间的关联。关联分析得到了12个p值为0.025的SNP，其中11个来自IRX1，其中包括最显著的SNP(p=0.000382)。这些SNPs中的一个与16q12正义上IRX3上游的HCNR具有87%的序列同源性，正在准备中。 4)对9号和16号染色体上的STRPs和SNPs进行统计遗传分析。在9号和16号染色体上进行精细定位以缩小先前识别的候选区域。连锁和关联研究确定了几个高度重要的区域，它们是正在准备的NextGen测序Miller等人的候选区域。 5)一项基于严重脊柱侧弯男性存在的研究，Miller等人提交。有严重曲度的男性由25个家庭(207个人)组成，其中至少有一个男性在青春期被诊断为侧弯。对质量性状和数量性状进行全基因组连锁分析，在第2、16和22号染色体上发现了显著的p值(2个相邻标记，p值为lt；0.01)。重要的SNPs主要存在于大基因的内含子中，对骨骼肌的发育和维持是不可或缺的，而SFI1负责染色体着丝粒复合体的完整性。 其他正在进行的大型合作包括： 1)NF1的临床特征(道格拉斯·斯图尔特博士，NIH/NCI) 2)ClinSeq项目(Les Biesecker，NIH/NHGRI) 3)GeneSTAR项目(约翰霍普金斯大学医学院Diane和Lewis Becker博士)Mathias等人，2010 4)印度糖尿病项目(约翰霍普金斯大学医学院Rasika Mathias博士) 5)爱尔兰人代谢物的变异(拉里·布罗迪博士，NIH/NHGRI)

Methods Development Virtually all of the theoretical work during the past year has focused on the development of Tiled regression, linear regression based methods for intra-familial tests of association for quantitative traits that address non-independence both at the marker and observational level. Tiled regression uses both multiple and stepwise regression methods in predefined segments of the genome, defined by hotspot blocks, to identify independent genetic variants responsible for the variation or susceptibility in quantitative and qualitative traits, respectively. Multiple and stepwise methods are used to test for associations on the sequence variants in each tile to select the independent markers within each tile. Higher order stepwise regressions are then used to identify significant variant across tiles, chromosomes and the entire genome. Quantitative and qualitative traits can be analyzed. With this approach, it becomes practical to analyze hundreds of thousands or millions of markers and their significant gene x gene interaction terms. This approach can substantially reduce the total number of tests to a number closer to the number of tiles rather than the number of markers. Furthermore, the tiled approach can be incorporated into a linear regression framework that allows for non-independence between observations incorporating features from the Regression of Offspring on Mid-Parant (ROMP) and Generalized Estimating Equations approaches. The tiled regression methodology has been implemented in TRAP, a software package written in the freely available R language. Functions are provided for assigning SNPs to hotspot-based tiles, data input, analysis and output of results. The package is structured modularly, so that it may be used as a single program or with user-written functions to allow for alternate tile definition or data format. This approach has been applied to both SNP data from fine mapping SNP studies with the scoliosis data in collaboration with Dr. Nancy Miller (U of Colorado), and two targeted candidate gene sequencing projects, an NF1 project in collaboration with Dr. Douglas Stewart and the ClinSeq project, in collaboration with Dr. Les Biesecker. Collaborations Familial Idiopathic Scoliosis Several analyses focusing on candidate regions and phenotypic subsets have been completed and manuscripts have either been submitted or are in preparation. These include: 1) In this study of susceptibility loci in FIS families with at least one individual with a triple curve, candidate regions have been identified on chromosomes 6 and 10 Marosy et al. 2010. 2) Statistical genetic analysis of two sets of families with familial idiopathic scoliosis with characteristics nearly identical to those of the sample analyzed in Miller et al. 2005. Linkage analysis and tests of association were performed in two regions on chromosome 1, previously identified as primary candidate regions. We have identified several regions of interest for subsequent nextgen sequencing Behnemann, doctoral thesis. 3) Targeted sequencing of the IRX gene family in families with kyphoscoliosis. We have identified an association between kyphoscoliosis and a sequence variant in an upstream conserved region of one of the IRX genes. Association analysis resulted in 12 SNPs with p-values < 0.025, of which 11 are 500 kb from IRX1, including the most significant SNP (p = 0.000382). One of these SNPs is in a HCNR sharing 87% sequence identity with a HCNR upstream from IRX3 on 16q12 Justice, in preparation. 4) Statistical genetic analysis of STRPs and SNPs on chromosomes 9 and 16. Fine mapping on chromosomes 9 and 16 was performed to narrow previously identified candidate regions. Linkage and association studies identified several highly significant regions that are candidates for nextgen sequencing Miller et al., in preparation. 5) A study based on the presence of males with severe scoliosis Miller et al., submitted. The males with severe curve subset was comprised of 25 families (207 individuals) in which at least one male was diagnosed in adolescence with a &#8805;30 lateral curvature. The genome-wide linkage analysis for the qualitative and quantitative traits resulted in significant p-values (2 adjacent markers with p-values < 0.01) on chromosomes 2, 16 and 22. Significant SNPs lie primarily in the introns of the LARGE gene, integral to the development and maintenance of skeletal muscle, and SFI1, responsible for the integrity of the chromosomal centromere complex. Other large ongoing collaborations include: 1) Clinical characterization of NF1 (Dr. Douglas Stewart, NIH/NCI) 2) the ClinSeq project (Les Biesecker, NIH/NHGRI) 3) the GeneSTAR project (Drs. Diane and Lewis Becker, Johns Hopkins University School of Medicine) Mathias et al., 2010 4) the India Diabetes Project (Dr. Rasika Mathias, Johns Hopkins University School of Medicine) 5) Variation in metabolites in the Irish (Dr. Larry Brody, NIH/NHGRI)